The present disclosure relates generally to the field of video quality monitoring, and in particular streaming media using Internet Protocol (IP) and General Packet Radio System Tunneling Protocol (GTP) or any similar current or future standards. More specifically, the present disclosure is related to monitoring and/or enhancing the video quality, such as for streaming media as seen by an end user on a mobile device 118.
Streaming media is an increasingly popular method for providing, and consuming, media products including various video and audio products, such as video on demand, Internet & television, streaming radio, and the like. However, data transport requirements for these applications exhibit high sensitivity to data loss and delivery time distortion and there are many factors that may impact the quality of the streaming media performance that can negatively impact the quality of service (QOS) and, thus, the subjective end user experience, quality of experience (QoE). A commonly experienced problem is a delay or freeze-frame experienced during consumption of the streaming media product, in which all media data downloaded to the client has been played, and the client is waiting for the next piece of media data. Such freezes, or hangs, give rise to user frustration and dissatisfaction.
In the interest of maintaining user satisfaction, and therefore user volume, streaming media providers are constantly working towards improving media distribution systems. However, given the complexity of streaming media distribution systems, it may often be difficult for a streaming media provider to determine what portions of the streaming media system are resulting in the greatest number of problems. As such, much resource allocation toward improving media distribution systems may often be expended relatively blindly. One root problem may be tied to the inability of media distribution providers to accurately assess the performance of their media distribution systems and identify the greatest weaknesses of the media distribution system.
As more users interact with streaming media on mobile devices 118 such as smart phones, pad computers, tablets, smart glasses, such as Google Glass, smart watches, and the like, the media distribution system expands to include the mobile telecommunications system where the distribution system used to deliver the streaming media may change as the users move geographically. The distribution system may move between cell towers, micro-cells, and the like and be influenced by the reception capability of the user's device.
There are various schemes to implement quality of service (QOS) on such networks to address the requirements of streaming media, especially when intermixed with conventional, time-insensitive, guaranteed delivery protocol stack data traffic. Furthermore, for efficiency reasons, the streaming media transport often uses a non-guaranteed delivery upper layer protocol stack such as UDP/IP making recovery of data in the presence of packet loss difficult. Regardless of whether QOS-enabled or non-QOS-enabled networks are employed, it is necessary to monitor the behavior of packet loss, delivery time distortion, and other real-time parameters of the network to assure satisfactory quality streaming media delivery. However, these schemes generally look at quality of service, QoS, only with respect to the delivery of individual data packets. Increasingly, videos are being transmitted as individual segments, where the delivery of the entire segment has more impact on video quality of experience than the delivery of individual packets.
Management Information Bases (MIBs) may include definitions for a number of network parameters such as packet loss, inter-arrival times, errors, percentage of network utilization, etc., whose purpose is to indicate to a network manager the general operating conditions of the network. Such traditional forms of monitoring network behavior cannot easily indicate the effects that network performance has on a single or a group of individual streaming media streams. Data gathering from MIBs operating across a range of network layers combined with a highly skilled and experienced practitioner would be required to simply determine the jitter imposed on a single MPEG video stream, for instance, and would only be possible by post-processing data gathered while the network was in operation. Determining the cause of a fault in a streaming media stream may be possible through such analysis but lacks the real-time indication of a network fault that is required to maintain high-quality networks such as for video or audio delivery. It also does not address the need to monitor large numbers of streams in real-time such as streams of Video-on-Demand (VoD) networks using less technically skilled operations personnel, as would be necessary to enable implementation of continuous cost-effective quality control procedures for widely deployed networks such as for VoD.
Histograms are often used to present the arrival time behavior of packets on a network, but such histograms may only represent the aggregate behavior of packets arriving at the measurement node due to the need to combine MIB data from a range of network layers to extract sufficient information to track a particular stream's performance. Traditional histograms define the jitter between any two packets. Streaming media requires more in-depth knowledge, such as the time variation across many packets referred to as the “network jitter growth”. This network jitter growth affects the streaming media quality as experienced by the user due to intermediate buffer overflow/underflow between the media source and its destination.
Network jitter growth of a media stream due to traffic congestion may also be an indicator of an impending fault condition and determining its presence as it begins to occur may be used to avoid transport failures, rather than simply to react to faults after they occur. Conventional post-processed MIB analysis is inadequate for these purposes as described above.
The concept of regulating stream flow in a network based on the leaky bucket paradigm describes a methodology that might be used to prevent intermediate buffer overflow and packet jitter by regulating the outflow of data based on a set of parameters configured to optimize a particular flow. This does not address the need to analyze and continuously monitor multiple streams as is required during the installation and operation of networks carrying streaming media, especially for those enterprises whose revenue is derived from the high quality delivery of streaming media, such as broadcast and cable television entities.
A common scheme used to effectively monitor multiple video streams is to decode each stream's MPEG content (for the video example) and display the streams on a large group of television screens. Monitoring personnel then watch the screens looking for any anomalous indications and take appropriate corrective action. This is a subjective and error prone process, as there is a possibility that a transient fault might be missed. This is also a reactive process, as corrective action may only be taken after a fault has occurred. Furthermore, this is also a relatively expensive process in terms of both equipment and personnel costs. It also provides little or no indications of the root cause of the fault, thus adding to the time required for implementing corrective action. This approach also does not easily scale to modern video delivery systems based upon emerging, cost-effective high-bandwidth, networks intended to transport thousands of independent video streams simultaneously. In addition, this approach cannot pinpoint the location of the fault. To do so, the personnel and equipment must be replicated at multiple points in the distribution network, greatly increasing the cost. For this to be effective, the personnel must monitor the same stream at exactly the same time for comparison.
Many types of network delivery impairments are transient in nature affecting a limited number of packets during a period of momentary traffic congestion, for example. Such impairments or impairment patterns may be missed using traditional monitoring personnel watching video monitors. By not recognizing possible repeating impairment patterns, faults may exist for much longer periods because after the fault has passed, there is no residual trace information available for analysis. The longer a fault persists, the worse the customer satisfaction levels, and the greater the potential for lost revenues.
Streaming Video-over-IP is a technology that allows the end user to watch video content over an IP network. Examples of Video-over-IP include Video on Demand (VoD), and IPTV (Internet Protocol Television). A Video-over-IP network may include a service provider network including one or more remote video servers a core network (e.g., Internet), and a local hub/edge switch, such as a cable television (CATV) hub or Digital Subscriber Line Access Multiplexers (DSLAMs). This network is then coupled to customer premises equipment (CPE) such as a set top box (STB) and television (often including various other home networking equipment), via a Network Interface Device (NID) typically located at a consumer's home. In addition to delivering streaming media to the consumer's home, the Video-over-IP technology allows the consumer to control the stream through the STB, enabling features such as channel changes (by selecting the particular stream(s) to be delivered), fast forward, pause, and rewind.
A disadvantage of conventional streaming media is that the quality of the IP stream may be degraded as it travels over the network before arriving at the end point (e.g., a consumer's television). Service providers may place monitors at various points along the network to measure the quality of the video stream being provides using one of a variety of quality of service (QOS) metrics. In this manner, service providers may relatively easily measure QOS at points between a remote video server and a customer premise equipment to isolate network problems occurring therein. However, the QOS of ultimate concern is that experienced by the consumer at the video destination or end point, such as the consumer's television.
When responding to a customer complaint of poor video quality, a service provider may initially check the network for QOS issues. In the event an acceptable QOS is detected at hub, service providers generally have no choice but to send a service technician to the consumer's premises to attempt to isolate the problem within the CPE. As many QOS problems are transient, a service technician may be required to make repeated visits to a consumer's home, at various times of day, in order to locate and properly diagnose the problem(s). It has been estimated that in many instances, the total cost to a service provider of sending a service technician to a consumer's home is at least $1,000.00 per visit. The service provider's inability to remotely monitor the quality of service from outside the consumer's home thus tends to be responsible for relatively high customer service costs.
As increasing amounts of audio visual (AV) content is distributed over-the-air to consumer's mobile devices using cellular networks, another layer of complexity is added to the tracking of video QOS on consumer devices. The identification of points of QOS degradation across a media delivery system that spans multiple modalities becomes complex.
Audio visual (AV) content is typically distributed from one or more central locations to local markets over relatively complex networks spanning large geographical areas. This AV content is typically supported by advertisements. However, while the AV content is typically generated for wide distribution, e.g., to national or international markets, advertisements are often intended for comparatively narrow distribution, such as for regional or local audiences. Advertisements are thus often spliced into the AV content at the regional or local level. To accommodate this splicing, the originator of the AV content, e.g., the television network, typically supplies the content with embedded markers, such as those commonly referred to as “avails” and/or SCTE-35 advertisement cue signals. Local content providers, such as network affiliate television stations and/or cable television (CATV) operators may use these avails to properly insert these local advertisements. A typical hour of AV content, for example, may be provided with four avails each corresponding to a 30 second time slot, or two avails each corresponding to a one minute time slot. These time slots are thus available for insertion of local advertisements.
Automated equipment is typically used to detect the avails and to splice in the local advertising content to minimize errors. However, errors may occur due to many factors, including bottlenecks or other traffic issues within the content distribution network, by errors in placement or detection of the avails, or simply by poor quality of the advertisements being supplied to the local insertion point. Local advertisements may thus be spliced in too early or too late, at audio levels that may be too high or too low, or in generally poor condition. This may result in the advertisements overrunning portions of the program, the advertisements being cut off, and/or the advertisements simply being of generally poor quality. These errors are not only problematic from a quality of experience (QoE), the viewer's subjective experience of watching the video which may be represented by a variety of performance metrics, but may also result in substantial refunds being due to advertisers who typically pay sizable fees for the airing of each 30 or 60 second advertisement.
A related problem pertains to verification of correct advertisement insertion. In this regard, it is often difficult for a local television station or CATV company to refute refund claims by advertisers who complain that their advertisements were improperly inserted, or were otherwise of poor quality at particular locations within the distribution network. Even in the event the quality of the program and advertisement are monitored by the television station or CATV company at the central insertion location, there is no guarantee that the quality of the content was satisfactory as experienced by the end user.
In an information-based society, the rate at which information is received and disseminated may prove crucial with respect to the value of that information. The value of this information may be even more greatly impacted in sectors in which the decisions being made that are associated with the data are highly dependent upon the freshness of such information.
For example, advertisers have limited advertising budgets and choose which television/radio stations to advertise on based upon the ratings of that particular television/radio station. Unfortunately, the information provided to the advertisers that is used to make such advertising determinations may be stale. For example, rating information is typically presented in ratings books, which are often only compiled and released every three months. Accordingly, this may result in decisions being made based upon aged data that may have since changed considerably.
Accordingly, a need exists for monitoring QOS for video content on consumer devices and, when a degradation in QOS is observed, identifying where the degradation occurred across a media distribution system having a plurality of transport modalities, video processing modules and computer systems.